by inferock · LLM Plugin · ★ 139
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npx inferock-bench
npx inferock-bench setup <provider>Taken from the project's README on GitHub.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 139 |
| Forks | 49 |
| Language | TypeScript |
| Category | LLM Plugin |
| Quality Score | 66/100 |
| Last Updated | 2026-08-17 |
| Created | 2026-07-02 |
| Platforms | claude-code, gemini, node |
| Est. Tokens | ~21k |
Your AI bill can be quietly wrong. Providers shouldn't get to grade their own bills. Today, the company that charges you also decides what counts as a failure, what gets credited, and keeps the only detailed records. inferock-bench puts an independent, per-call receipt of what you were billed — and what failed — in your own hands.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: